A review of the AURORA and BLISS trials: will it revolutionize the treatment of lupus nephritis?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Renal involvement in patients with systemic lupus erythematosus can lead to significant complications including end-stage renal disease. Treatment of lupus nephritis has evolved over the last several decades, but despite this evolution, many patients fail to achieve remission and often progress to end-stage kidney disease or carry a burden of adverse side effects related to treatment. RECENT FINDINGS: The recent findings from AURORA 1 and BLISS LN trials led the FDA to approve voclosporin and belimumab for the treatment of lupus nephritis. The AURORA 1 trial demonstrated that voclosporin, a second-generation calcineurin inhibitor, effectively lowers proteinuria in patients with lupus nephritis, when added to mycophenolate mofetil with a better safety profile, compared with other calcineurin inhibitors. The BLISS LN trial revealed better control of disease and lower risk of progression to end stage kidney disease (ESKD) and relapses in patients treated with belimumab in addition to standard therapy. SUMMARY: Both voclosporin and belimumab are costly and have not shown any early evidence to revolutionize practice in the management of lupus nephritis. Until more data are made available with future studies or other cost-effective treatment options become available, the widespread adoption and utility of these novel agents remains limited.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".